Audio splicing detection and localization using multistage filterbank spectral sketches and decision fusion

Heterogeneous audio splicing tampering, which combines audio recordings from different scenarios or devices, has posed a significant challenge to audio authenticity. Most of the existing work is not good at the detection and localization for multiple splicing points, especially when the signal-to-noise ratios (SNRs) of recordings involved in splicing are close. In this work, we propose an audio splicing detection and localization method on the basis of multistage filterbank spectral sketches (MFBSS) and decision fusion. More specifically, we first remove the silent segments to reduce the redundant information and estimate the background noise of the combined voice-only segments. Next, to obtain more audio details, we propose a feature fusion strategy to extract the MFBSS feature from the background noise. Then, we develop a decision fusion strategy to detect and localize all the possible splicing points. Finally, we evaluate our method against the state-of-the-art splicing detection approaches on public datasets with various noises and SNR differences. Experimental results demonstrate that the proposed approach is effective for various noise scenarios with small SNR differences and is also robust against anti-forensics attacks.